At times, it can be difficult for an online user to shop for products or find an appropriate product or service online. This is especially true when the user does not know exactly what he or she is looking for. Consumers, for example, expect to be able to input minimal information as search criteria and, in response, get specific, targeted and relevant information. The ability to consistently match a product or service to a consumer's request for a recommendation is a very valuable tool, as it can result in a high volume of sales for a particular product or company. Unfortunately, effectively accommodating these demands using existing search and recommendation technologies requires substantial time and resources, which are not easily captured into a search engine or recommendation system. The difficulties of this process are compounded by the unique challenges that online stores and advertisers face to make products and services known to consumers in this dynamic online environment.
Recommendation technology exists that attempts to predict items, such as movies, music and books that a user may be interested in, usually based on some information about the user's profile. Often, this is implemented as a collaborative filtering algorithm. Collaborative filtering algorithms typically analyze the user's past behavior in conjunction with the other users of the system. Ratings for products are collected from all users forming a collaborative set of related “interests” (e.g., “users that liked this item, have also like this other one”). In addition, a user's personal set of ratings allows for statistical comparison to a collaborative set and the formation of suggestions. Collaborative filtering is the recommendation system technology that is most common in current e-commerce systems. It is used in several vendor applications and online stores, such as Amazon.com.
Unfortunately, recommendation systems that use collaborative filtering are dependent on quality ratings, which are difficult to obtain because only a small set of users of the e-commerce system take the time to accurately rate products. Further, click-stream and buying behavior as ratings are often not connected to interests because the user navigation pattern through the e-commerce portal will not always be a precise indication of the user buying preferences. Additionally, a critical mass is difficult to achieve because collaborative rating relies on a large number of users for meaningful results, and achieving a critical mass limits the usefulness and applicability of these systems to a few vendors. Moreover, new users and new items require time to build history, and the statistical comparison of items relies on user ratings of previous selections. Furthermore, there is limited exposure of the “long tail,” such that the limitation on the growth of human-generated ratings limits the number of products that can be offered and have their popularity measured.
The long tail is a common representation of measurements of past consumer behavior. The theory of the long tail is that economy is increasingly shifting away from a focus on a relatively small number of “hits” (e.g., mainstream products and markets) at the head of the demand curve and toward a huge number of niches in the tail. FIG. 1 is a graph illustrating an example of the long tail phenomenon showing the measurement of past demand for songs, which are ranked by popularity on the horizontal axis. As illustrated in FIG. 1, the most popular songs 120 are made available at brick-and-mortar (B&M) stores and online while the least popular songs 130 are made available only online.
To compound problems, most traditional e-commerce systems make overspecialized recommendations. For instance, if the system has determined the user's preference for books, the system will not be capable of determining the user's preference for songs without obtaining additional data and having a profile extended, thereby constraining the recommendation capability of the system to just a few types of products and services.
There are rule-based recommendation systems that rely on user input and a set of pre-determined rules which are processed to generate output recommendations to users. A web portal, for example, gathers input to the recommendation system that focuses on user profile information (e.g., basic demographics and expressed category interests). The user input feeds into an inference engine that will use the pre-determined rules to generate recommendations that are output to the user. This is one simple form of recommendation systems, and it is typically found in direct marketing practices and vendor applications.
However, it is limited in that it requires a significant amount of work to manage rules and offers (e.g., the administrative overhead to maintain and expand the set of rules can be considerably large for e-commerce systems). Further, there is a limited number of pre-determined rules (e.g., the system is only as effective as its set of rules). Moreover, it is not scalable to large and dynamic e-commerce systems. Finally, there is limited exposure of the long tail (e.g., the limitation on the growth of a human-generated set of inference rules limits the number of products that can be offered and have their popularity measured).
Content-based recommendation systems exist that analyze content of past user selections to make new suggestions that are similar to the ones previously selected (e.g., “if you liked that article, you will also like this one”). This technology is based on the analysis of keywords present in the text to create a profile for each of the documents. Once the user rates one particular document, the system will understand that the user is interested in articles that have a similar profile. The recommendation is created by statistically relating the user interests to the other articles present in a set. Content-based systems have limited applicability, as they rely on a history being built from the user's previous accesses and interests. They are typically used in enterprise discovery systems and in news article suggestions.
In general, content-based recommendation systems are limited because they suffer from low degrees of effectiveness when applied beyond text documents because the analysis performed relies on a set of keywords extracted from textual content. Further, the system yields overspecialized recommendations as it builds an overspecialized profile based on history. If, for example, a user has a user profile for technology articles, the system will be unable to make recommendations that are disconnected from this area (e.g., poetry). Further, new users require time to build history because the statistical comparison of documents relies on user ratings of previous selections.